From: Peter Dalgaard <p.dalgaard_at_biostat.ku.dk>

Date: Thu 01 Dec 2005 - 01:11:45 EST

Date: Thu 01 Dec 2005 - 01:11:45 EST

"anders superanders" <andersdetermigigen@hotmail.com> writes:

> Hi I was wondering if there is a permutation test available in R for linear

*> models with continuous dependent covariates. I want to do a test like the
**> one shown here.
**>
**> bmi<-rnorm(100,25)
**> x<-c(rep(0,75),rep(1,25))
**> y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x
**>
**> H0<-lm(y~1+x+bmi)
**> H1<-lm(y~1+x+bmi+x*bmi)
**> anova(H0,H1)
**> summary(lm(y~1+x+bmi))
**>
**>
**> But I want to use permutation testing to avoid an inflated p-value due to a
**> y that is not totally normal distributed and I do not want to log transform
**> y.
*

Er, what would you permute? For an interaction test like this (notice by the way that "*" in your model formula does not mean what you think it does) I do not think a permutation test exists. You could try bootstrapping to get an improved approximation the distribution of the interaction term.

-- O__ ---- Peter Dalgaard ุster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk) FAX: (+45) 35327907 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Thu Dec 01 02:07:06 2005

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